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Robust selection of variables in linear discriminant analysis

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Abstract

A commonly used procedure for reduction of the number of variables in linear discriminant analysis is the stepwise method for variable selection. Although often criticized, when used carefully, this method can be a useful prelude to a further analysis. The contribution of a variable to the discriminatory power of the model is usually measured by the maximum likelihood ratio criterion, referred to as Wilks’ lambda. It is well known that the Wilks’ lambda statistic is extremely sensitive to the influence of outliers. In this work a robust version of the Wilks’ lambda statistic will be constructed based on the Minimum Covariance Discriminant (MCD) estimator and its reweighed version which has a higher efficiency. Taking advantage of the availability of a fast algorithm for computing the MCD a simulation study will be done to evaluate the performance of this statistic.

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  1. Austro Control GmbH, Vienna, Austria

    Valentin Todorov

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  1. Valentin Todorov
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Correspondence to Valentin Todorov.

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The presentation of material in this article does not imply the expression of any opinion whatsoever on the part of Austro Control GmbH and is the sole responsibility of the authors.

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Todorov, V. Robust selection of variables in linear discriminant analysis. Stat. Meth. & Appl. 15, 395–407 (2007). https://doi.org/10.1007/s10260-006-0032-6

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  • DOI: https://doi.org/10.1007/s10260-006-0032-6

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